


import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)







def 							__magic_name__    (     lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_					:				Union[str, Any]              =  model.config

	lowerCamelCase_					:				Any              =  DonutSwinConfig(
	    image_size=original_config.input_size     ,	patch_size=4     ,	depths=original_config.encoder_layer     ,	num_heads=[4, 8, 16, 32]     ,	window_size=original_config.window_size     ,	embed_dim=128     ,	)
	lowerCamelCase_					:				int              =  MBartConfig(
	    is_decoder=_lowercase     ,	is_encoder_decoder=_lowercase     ,	add_cross_attention=_lowercase     ,	decoder_layers=original_config.decoder_layer     ,	max_position_embeddings=original_config.max_position_embeddings     ,	vocab_size=len(
	        model.decoder.tokenizer)     ,	scale_embedding=_lowercase     ,	add_final_layer_norm=_lowercase     ,	)

	return encoder_config, decoder_config







def 							__magic_name__    (     lowerCAmelCase_):
	'''simple docstring'''


	if "encoder.model" in name:
		lowerCamelCase_					:				Optional[int]              =  name.replace("encoder.model"     ,	"encoder")
	if "decoder.model" in name:
		lowerCamelCase_					:				int              =  name.replace("decoder.model"     ,	"decoder")
	if "patch_embed.proj" in name:
		lowerCamelCase_					:				str              =  name.replace("patch_embed.proj"     ,	"embeddings.patch_embeddings.projection")
	if "patch_embed.norm" in name:
		lowerCamelCase_					:				List[str]              =  name.replace("patch_embed.norm"     ,	"embeddings.norm")
	if name.startswith("encoder"):
		if "layers" in name:
			lowerCamelCase_					:				int              =  "encoder." + name
		if "attn.proj" in name:
			lowerCamelCase_					:				Dict              =  name.replace("attn.proj"     ,	"attention.output.dense")
		if "attn" in name and "mask" not in name:
			lowerCamelCase_					:				str              =  name.replace("attn"     ,	"attention.self")
		if "norm1" in name:
			lowerCamelCase_					:				Dict              =  name.replace("norm1"     ,	"layernorm_before")
		if "norm2" in name:
			lowerCamelCase_					:				str              =  name.replace("norm2"     ,	"layernorm_after")
		if "mlp.fc1" in name:
			lowerCamelCase_					:				str              =  name.replace("mlp.fc1"     ,	"intermediate.dense")
		if "mlp.fc2" in name:
			lowerCamelCase_					:				Any              =  name.replace("mlp.fc2"     ,	"output.dense")

		if name == "encoder.norm.weight":
			lowerCamelCase_					:				Optional[int]              =  "encoder.layernorm.weight"
		if name == "encoder.norm.bias":
			lowerCamelCase_					:				Dict              =  "encoder.layernorm.bias"

	return name







def 							__magic_name__    (     lowerCAmelCase_     ,	lowerCAmelCase_):
	'''simple docstring'''


	for key in orig_state_dict.copy().keys():
		lowerCamelCase_					:				List[Any]              =  orig_state_dict.pop(_lowercase)

		if "qkv" in key:
			lowerCamelCase_					:				Tuple              =  key.split(".")
			lowerCamelCase_					:				Dict              =  int(key_split[3])
			lowerCamelCase_					:				Union[str, Any]              =  int(key_split[5])
			lowerCamelCase_					:				Union[str, Any]              =  model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

			if "weight" in key:
				lowerCamelCase_					:				int              =  val[:dim, :]
				lowerCamelCase_					:				Dict              =  val[dim : dim * 2, :]
				lowerCamelCase_					:				Any              =  val[-dim:, :]
			else:
				lowerCamelCase_					:				Any              =  val[:dim]
				lowerCamelCase_					:				Any              =  val[dim : dim * 2]
				lowerCamelCase_					:				Any              =  val[-dim:]
		elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
			# HuggingFace implementation doesn't use attn_mask buffer
			# and model doesn't use final LayerNorms for the encoder
			pass
		else:
			lowerCamelCase_					:				Optional[int]              =  val

	return orig_state_dict







def 							__magic_name__    (     lowerCAmelCase_     ,	lowerCAmelCase_=None     ,	lowerCAmelCase_=False):
	'''simple docstring'''


	lowerCamelCase_					:				int              =  DonutModel.from_pretrained(_lowercase).eval()

	# load HuggingFace model
	lowerCamelCase_       ,lowerCamelCase_					:				Optional[int]              =  get_configs(_lowercase)
	lowerCamelCase_					:				List[str]              =  DonutSwinModel(_lowercase)
	lowerCamelCase_					:				Optional[int]              =  MBartForCausalLM(_lowercase)
	lowerCamelCase_					:				Dict              =  VisionEncoderDecoderModel(encoder=_lowercase     ,	decoder=_lowercase)
	model.eval()

	lowerCamelCase_					:				Union[str, Any]              =  original_model.state_dict()
	lowerCamelCase_					:				Tuple              =  convert_state_dict(_lowercase     ,	_lowercase)
	model.load_state_dict(_lowercase)

	# verify results on scanned document
	lowerCamelCase_					:				List[Any]              =  load_dataset("hf-internal-testing/example-documents")
	lowerCamelCase_					:				Optional[Any]              =  dataset["test"][0]["image"].convert("RGB")

	lowerCamelCase_					:				str              =  XLMRobertaTokenizerFast.from_pretrained(_lowercase     ,	from_slow=_lowercase)
	lowerCamelCase_					:				List[str]              =  DonutImageProcessor(
	    do_align_long_axis=original_model.config.align_long_axis     ,	size=original_model.config.input_size[::-1])
	lowerCamelCase_					:				List[str]              =  DonutProcessor(_lowercase     ,	_lowercase)
	lowerCamelCase_					:				Any              =  processor(_lowercase     ,	return_tensors="pt").pixel_values

	if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
		lowerCamelCase_					:				int              =  "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
		lowerCamelCase_					:				str              =  "When is the coffee break?"
		lowerCamelCase_					:				Dict              =  task_prompt.replace("{user_input}"     ,	_lowercase)
	elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
		lowerCamelCase_					:				int              =  "<s_rvlcdip>"
	elif model_name in [
	    "naver-clova-ix/donut-base-finetuned-cord-v1",
	    "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
	]:
		lowerCamelCase_					:				Optional[int]              =  "<s_cord>"
	elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
		lowerCamelCase_					:				List[Any]              =  "s_cord-v2>"
	elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
		lowerCamelCase_					:				Optional[Any]              =  "<s_zhtrainticket>"
	elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
		# use a random prompt
		lowerCamelCase_					:				List[str]              =  "hello world"
	else:
		raise ValueError("Model name not supported")
	lowerCamelCase_					:				List[str]              =  original_model.decoder.tokenizer(_lowercase     ,	add_special_tokens=_lowercase     ,	return_tensors="pt")[
	    "input_ids"
	]

	lowerCamelCase_					:				Tuple              =  original_model.encoder.model.patch_embed(_lowercase)
	lowerCamelCase_       ,lowerCamelCase_					:				int              =  model.encoder.embeddings(_lowercase)
	assert torch.allclose(_lowercase     ,	_lowercase     ,	atol=1E-3)

	# verify encoder hidden states
	lowerCamelCase_					:				str              =  original_model.encoder(_lowercase)
	lowerCamelCase_					:				Optional[Any]              =  model.encoder(_lowercase).last_hidden_state
	assert torch.allclose(_lowercase     ,	_lowercase     ,	atol=1E-2)

	# verify decoder hidden states
	lowerCamelCase_					:				Union[str, Any]              =  original_model(_lowercase     ,	_lowercase     ,	_lowercase).logits
	lowerCamelCase_					:				Any              =  model(_lowercase     ,	decoder_input_ids=_lowercase).logits
	assert torch.allclose(_lowercase     ,	_lowercase     ,	atol=1E-3)
	print("Looks ok!")

	if pytorch_dump_folder_path is not None:
		print(F"""Saving model and processor to {pytorch_dump_folder_path}""")
		model.save_pretrained(_lowercase)
		processor.save_pretrained(_lowercase)

	if push_to_hub:
		model.push_to_hub("nielsr/" + model_name.split("/")[-1]     ,	commit_message="Update model")
		processor.push_to_hub("nielsr/" + model_name.split("/")[-1]     ,	commit_message="Update model")


if __name__ == "__main__":
							__magic_name__  	=     argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--model_name''',
							    default='''naver-clova-ix/donut-base-finetuned-docvqa''',
							    required=False,
							    type=str,
							    help='''Name of the original model you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default=None,
							    required=False,
							    type=str,
							    help='''Path to the output PyTorch model directory.''',
							)
							parser.add_argument(
							    '''--push_to_hub''',
							    action='''store_true''',
							    help='''Whether or not to push the converted model and processor to the 🤗 hub.''',
							)

							__magic_name__  	=     parser.parse_args()
							convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""



import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def      UpperCAmelCase     (       _lowercase :   Union[str, Any]	)						-> Union[str, Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			os.path.join(args.tf_model_dir			,   '''parameters.json'''	)
	lowerCAmelCase_      							=			json.loads(open(_lowercase	).read()	)
	if not params:
		raise ValueError(
		    F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""	)
	if not args.output.endswith('''.pt'''	):
		lowerCAmelCase_      							=			args.output + '''.pt'''
	lowerCAmelCase_      							=			OrderedDict()
	with tf.device('''/CPU:0'''	):
		lowerCAmelCase_      							=			tf.train.load_checkpoint(args.tf_model_dir	)
		lowerCAmelCase_      							=			reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			lowerCAmelCase_      							=			reader.get_tensor(_lowercase	).astype(np.floataa	)
			if key_name.endswith('''/adam_m'''	) or key_name.endswith('''/adam_v'''	):
				continue
			if key_name.startswith('''pasts/'''	):
				if key_name.startswith('''pasts/mlp'''	):
					lowerCAmelCase_      							=			int(key_name[9]	)
				elif key_name.startswith('''pasts/out'''	):
					lowerCAmelCase_      							=			8
				lowerCAmelCase_      							=			'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/moe'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/switch_gating/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/softmlp/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/wo/kernel'''	) or key_name.endswith('''/wi/kernel'''	):
					lowerCAmelCase_      							=			key_name[-9:-7]
					for i in range(1_6	):
						lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
						lowerCAmelCase_      							=			(
						    vnp[i].transpose([1, 0]	).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/mlp'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/p1/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p1/bias'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p2/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p2/bias'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/ln'''	):
				lowerCAmelCase_      							=			int(key_name[8:].split('''/'''	)[0]	)
				if key_name.endswith('''/b'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.norm.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/g'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.norm.weight''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/att'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/qkv/kernel'''	):
					lowerCAmelCase_      							=			vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					lowerCAmelCase_      							=			state[:, 0, :, :]
					lowerCAmelCase_      							=			state[:, 1, :, :]
					lowerCAmelCase_      							=			state[:, 2, :, :]
					lowerCAmelCase_      							=			(
					    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			(
					    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			(
					    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/o/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
					lowerCAmelCase_      							=			(
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]	).transpose([1, 0]	).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/an'''	):
				lowerCAmelCase_      							=			int(key_name[8:].split('''/'''	)[0]	)
				if key_name.endswith('''/b'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.norm.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/g'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.norm.weight''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif (
			    key_name.startswith('''model/wte'''	)
			    or key_name.startswith('''model/wpe'''	)
			    or key_name.startswith('''model/ete'''	)
			):
				lowerCAmelCase_      							=			{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
				    key_name[-3:]
				]
				lowerCAmelCase_      							=			'''model.%s.weight''' % nlayer
				lowerCAmelCase_      							=			vnp.copy()  # same in embedded
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				if key_name.startswith('''model/wte'''	):
					lowerCAmelCase_      							=			'''lm_head.weight'''
					lowerCAmelCase_      							=			vnp.copy()  # same in embedded
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/wob'''	):
				lowerCAmelCase_      							=			'''final_logits_bias'''
				lowerCAmelCase_      							=			vnp.copy()  # same in embedded
				lowerCAmelCase_      							=			state.reshape((1, -1)	)
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name == "model/dense/kernel":
				lowerCAmelCase_      							=			'''model.last_project.weight'''
				lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name == "model/dense_1/bias":
				lowerCAmelCase_      							=			'''model.last_project.bias'''
				lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
	torch.save(_lowercase			,   args.output	)


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser(
		    description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
		)
		parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
		parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
		lowercase_							=							parser.parse_args()
		convert_tf_gptsan_to_pt(args)